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Check out the new article: MLP (Multilayer Perceptron) Inside an MQL5 Expert Advisor: Training on Historical Data Without Python or Weight Files.
You are developing an MQL5 Expert Advisor and want to add an ML direction filter, but the "train in Python → export weights → load the file" workflow constantly creates technical risks: normalization mismatch, lost weights or the wrong weight version, and discrepancies in feature logic between the offline environment and the terminal. As a result, the filter may behave erratically, and debugging turns into a search for differences between the two code bases.
The purpose of this article is deliberately practical: not to build a "large neural model," but to develop a compact MLP gate that is trained on the history of the current symbol and timeframe, is periodically retrained, and operates only on closed bars. While this filter does not replace a full-fledged research ML pipeline, it solves a specific engineering problem for the Expert Advisor: eliminating external dependencies and integrating training, features, normalization, and inference into a single .mq5 file.
Previously, the project’s MLP was trained offline in Python, and a file containing its weights was exported and loaded into the Expert Advisor. This approach worked, but it regularly caused operational problems: different scalers, different feature versions, forgotten files in MQL5\Files, and difficult troubleshooting when behavior in the tester did not match what was expected from the Python model. Here, we propose a different approach: training a small MLP directly within the Expert Advisor itself using historical data from the same instrument on which it will be applied.
Author: Galym Yechshanov